While rapid detection of positive COVID-19 cases is of utmost importance, physicians and healthcare personnel are overwhelmed with increasing number of patients in need of immediate care and treatment. In other words, expert thoracic radiologists may not be available at all times to diagnose positive cases in a timely fashion, leading to not only delays in treatment, but also further transmission of the virus by patients who are not promptly isolated. Recent studies [1]}, [2]} have demonstrated the capabilities of artificial intelligence (AI) models in achieving human-level performance in detection of lung nodules from LDCT. Motivated by this, in this study we aim at developing an AI model based on deep learning architectures to distinguish COVID-19 from community acquired pneumonia (CAP) and normal cases, using LDCT and ULDCT. To the best of our knowledge, Reference [3]} is the only study considering LDCT in AI-based COVID-19 analysis, by simulating standard dose scans from low dose ones. The aforementioned study, however, uses synthesized data, i.e., it does not use real LDCT/ULDCT data from COVID-19 individuals, and does not deal with the disease diagnosis, which is the main focus of our research. We hypothesize that AI can achieve a human-level performance in diagnosing COVID-19 based on LDCT and ULDCT scans.
